提出用语义吸引子构建具备意图的通用人工智能，让语言理解走向意义稳定。
Semantic Attractors and the Emergence of Meaning: Towards a Teleological Model of AGI
- 用复数空间中的张量递归变换构建语义结构，模拟讽刺与歧义。
- 引入语义吸引子作为意图驱动的稳定性机制，实现意义收敛。
- 适合关注认知架构、语义本质与未来AGI设计的研究者。
本文提出一种基于复值语义空间中语义吸引子的语义型通用人工智能理论框架。不同于当前基于统计预测的Transformer语言模型，该模型通过涉及虚数单位 $i$ 的循环运算，构建可表征讽刺、同音异义和模糊性的旋转语义结构。核心是语义吸引子——一种具有目的性的算子（称为 Microvitum），它不依赖概率推断，而是以梯度流、张量变形和迭代矩阵动态为机制，引导意义向稳定、清晰与表达深度收敛。我们认为，真正意义并非来自模拟，而是源于对语义一致性的递归逼近，这需要一种能塑造语言而非仅预测语言的根本性认知架构。
原文摘要 · Abstract (English)
This essay develops a theoretical framework for a semantic Artificial General Intelligence (AGI) based on the notion of semantic attractors in complex-valued meaning spaces. Departing from current transformer-based language models, which operate on statistical next-token prediction, we explore a model in which meaning is not inferred probabilistically but formed through recursive tensorial transformation. Using cyclic operations involving the imaginary unit \emph{i}, we describe a rotational semantic structure capable of modeling irony, homonymy, and ambiguity. At the center of this model, however, is a semantic attractor -- a teleological operator that, unlike statistical computation, acts as an intentional agent (Microvitum), guiding meaning toward stability, clarity, and expressive depth. Conceived in terms of gradient flows, tensor deformations, and iterative matrix dynamics, the attractor offers a model of semantic transformation that is not only mathematically suggestive, but also philosophically significant. We argue that true meaning emerges not from simulation, but from recursive convergence toward semantic coherence, and that this requires a fundamentally new kind of cognitive architecture -- one designed to shape language, not just predict it.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。